Modeling and state of health estimation of nickel–metal hydride battery using an EPSO-based fuzzy c-regression model

Telmoudi, Achraf Jabeur; Soltani, Moez; Belgacem, Yassine Ben; Châari, Abdelkader

doi:10.1007/s00500-019-04343-2

Cited by 18 publications

(10 citation statements)

References 34 publications

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“…Beyond impedance spectroscopy, examples of model-based approaches to SoH estimation include Kalman filtering [23,24] or, more recently, generalised approaches using a fuzzy c-regression model incorporating particle swarm optimisation [25], which has seen applied in SoH estimation for a NiMH battery [26]. Further, filtering-based methods include [27,28] for decoupled estimations of SoC and SoH, employing a recursive least-squares-based SoH estimator with online-identified ECM and with SoC estimation using a Kalman filter and H-infinity filter, respectively.…”

Section: Eis and Model-based State Of Health Estimationmentioning

confidence: 99%

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Rastegarpanah

Hathaway

2021

Energies

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The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.

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Section: Eis and Model-based State Of Health Estimationmentioning

confidence: 99%

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Rastegarpanah

Hathaway

2021

Energies

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“…The accuracies for these approaches lie with RMS prediction errors between 2.4% and 5%, ultimately suggesting there is scope for further study and improvement in this area. Beyond the ECM approach to battery modelling, more generalised approaches such as fuzzy c-regression have been introduced for parameter estimation of non-linear systems by Jabeur Telmoudi et al 45 and later extended to modelling batteries in Telmoudi et al 46 in which a fuzzy c-regression model with Euclidean particle swarm optimisation is employed to build a model of an NiMH battery under cycling, which is used to estimate SoH with high accuracy. However, although such methods are capable of modelling the battery behaviour with potentially higher accuracy than an ECM-based approach, it is unclear how these parameters are related to physical degradation phenomena occurring within the cell.…”

Section: Background and Related Workmentioning

confidence: 99%

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Rastegarpanah

Hathaway

Ahmeid

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.

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“…At present, the method of SOH estimation can be divided into two categories, the first category is the mathematical model method that needs to consider the internal chemical mechanism of the lithium-ion battery, and the second category is the data-driven method that indirectly predicts based on the measurement data. Model-based method mainly include electrochemical model [4] [5] and equivalent circuit model [6] [7]. The electrochemical model mainly calculates the accurate SOH by studying the internal electrochemical reaction of the battery.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Neural Network Model For Battery State Of Health

Zhou

2022

Preprint

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Accurate estimation of the state of health (SOH) of lithium-ion batteries is the key to ensure the safe use of lithium-ion batteries. In practice, the application of traditional health features is hindered by incomplete charge and discharge. When the battery is stably charged, the voltage and temperature of the battery under different health states show similar spatial degradation trends. Therefore, the degradation trend of voltage and temperature is directly taken as the health characteristic sequence through the dynamic time warping barycenter averaging (DBA) clustering. In addition, a new model attention depthwise temporal convolutional network (AD-TCN) considering health characteristics is proposed for SOH estimation. Depthwise separable convolution operation is used to extend temporal convolutional network (TCN) to a model suitable for multivariate prediction. Depthwise convolution is used as feature extractor, and pointwise convolution recombines all features for regression prediction. In addition, the convolutional block attention module is used in the channel dimension and spatial dimension to selectively enhance or suppress the details. Experiments on NASA data sets show that this method has strong reliability and high prediction accuracy.

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Modeling and state of health estimation of nickel–metal hydride battery using an EPSO-based fuzzy c-regression model

Cited by 18 publications

References 34 publications

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

A Lightweight Neural Network Model For Battery State Of Health

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